The post NVIDIA’s cuML Enhances Tree-Based Model Inference with Forest Inference Library appeared on BitcoinEthereumNews.com.

Darius Baruo
Jun 05, 2025 07:57

NVIDIA’s cuML 25.04 introduces enhancements to the Forest Inference Library, boosting tree-based model inference performance with new features and optimizations.

NVIDIA has announced significant updates to its Forest Inference Library (FIL) as part of the cuML 25.04 release, aimed at supercharging the performance of tree-based model inference. This enhancement focuses on achieving faster and more efficient inference for gradient-boosted trees and random forests, particularly when trained in frameworks like XGBoost, LightGBM, and scikit-learn, according to NVIDIA. New Features and Optimizations One of the key updates includes a redesigned C++ implementation that supports batched inference on both GPU and CPU. The updated FIL boasts an optimize() function for tuning inference models and introduces advanced inference APIs such as predict_per_tree and apply. Notably, the new version promises up to a fourfold increase in GPU throughput compared to the previous FIL version. The auto-optimization feature is a standout, simplifying the process of fine-tuning performance with a built-in method that adjusts hyperparameters for optimal performance based on batch size. This is particularly beneficial for users aiming to leverage FIL’s capabilities without the need for extensive manual configuration. Performance Benchmarks In performance tests, cuML 25.04 demonstrated significant speed improvements over its predecessor. Across a variety of model parameters and batch sizes, the new FIL version outperformed the previous one in 75% of scenarios, achieving a median speedup of 1.16x. The enhancements were particularly evident in scenarios requiring batch size 1 performance and maximum throughput. Compared to scikit-learn’s native execution, FIL’s performance was notably superior, with speedups ranging from 13.9x to 882x, depending on the model and batch size. These improvements highlight FIL’s potential to replace more resource-intensive CPU setups with a single GPU, offering both speed and cost efficiency. Broad Applicability…

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Filed under: News - @ June 6, 2025 12:22 pm